2 August 2006. Sure, you might need a $250,000 sequencer to do genetics these days, but sometimes a well-trained pair of eyes is all you need to spot a genetic anomaly. A study published July 4 in the early online Molecular Psychiatry by Ben Pickard and colleagues at the University of Edinburgh in Scotland describes evidence that disruption of the GRIK4 (KA1) glutamate receptor gene underlies a case of schizophrenia. They also provide association data linking the gene to both schizophrenia and bipolar disorder (see SRF live discussion on common etiology of psychotic disorders).

But it all begins with an experienced cytologist, like author Pat Malloy, simply lining up some chromosomes in the microscope and spotting one that doesn’t look quite right. Staining will reveal bands of high and low gene density that characterize individual chromosomes. To most of us, it might seem like trying to identify individual zebras by their stripes, but to the well-trained eye, the bands not only identify the chromosomes, they also reveal deletions of entire regions or movement of a region from one chromosome to another (translocations). (For those of us who are genetics-challenged, much fun and some knowledge about these processes can be had at the University of Utah’s Genetic Science Learning Center. Don’t miss the Make a Karyotype game.)

The study of such rare chromosomal abnormalities is a specialty of the Edinburgh group led by David Porteous, and in this case, Pickard, Walter Muir, and colleagues found that a patient with mild learning disability (mental retardation) and schizophrenia has disruptions involving chromosomes 2, 8, and 11. The breakpoints disrupt a number of genes; however, the researchers found the GRIK4 gene the most promising candidate for involvement in the patient’s clinical picture. The breakpoint on chromosome 11 lies between exons 2 and 3 of the GRIK4 gene, a break that is predicted to lead to an inactive gene product. GRIK4 is a member of the ionotropic glutamate receptor family (specifically in the kainate responsive group—less well-known than the NMDA and AMPA glutamate receptors). Two other disrupted genes found in this region of chromosome 11 but not investigated in this study were PKNOX2/PREP2, which codes for a transcription factor, and RP26/CERKL, coding for a gene that may be involved in retinitis pigmentosa.

Beyond the large body of data implicating glutamate dysfunction in schizophrenia (see SRF Current Hypothesis paper by Moghaddam), the authors note that GRIK4 lies in a chromosomal region with some evidence for linkage to the disorder. It is also preferentially expressed in areas of the brain that are of particular relevance to schizophrenia, such as amygdala, hippocampus, and entorhinal cortex. In a case control study of karyotypically normal subjects (368 schizophrenia, 368 bipolar, and 458 normal subjects), employing 27 SNP markers, Pickard and colleagues found three SNPs and a haplotype associated with schizophrenia risk and two SNPs and a haplotype with a protective effect for bipolar disorder. The schizophrenia-linked loci were in the N-terminal (extracellular) coding region, whereas the bipolar-linked loci were in the C-terminal (cytoplasmic) coding region.

“In this patient, we believe that haploinsufficiency of this gene is most likely responsible for the psychiatric component of the patient’s diagnosis although…we cannot definitively rule out other mechanisms,” the authors conclude.—Hakon Heimer.

The paper by Walsh et al. is an important addition to the expanding literature on copy number variations in the human genome and their potential role in causing neuropsychiatric disorders. It is clear that copy number variations are important aspects of human genetic variation and that deletions and duplications in diverse genes throughout the genome are likely to affect the function of these genes and possibly the development and function of the human brain. So-called private variations, such as those described in this paper, i.e., changes in the genome found in only a single individual, as all of these variations are, are difficult to establish as pathogenic factors, because it is hard to know how much they contribute to the complex problem of human behavioral variation in a single individual. If the change is private, i.e., only in one case and not enriched in cases as a group, as are common genetic polymorphisms such as SNPs, how much they account for case status is very difficult to prove.

An assumption implicit in this paper is that these private variations may be major factors in the case status of the individuals who have them. The data of this paper suggest, however, this is actually not the case, at least for the childhood onset cases. Here’s why: mentioned in the paper is a statement that only two of the CNVs in the childhood cases are de novo, i.e., spontaneous and not inherited (and one of these is on the Y chromosome, making its functional implications obscure). If most of the CNVs are inherited, they can’t be causing illness per se as major effect players because they are coming from well parents.

Also, if you add up all CNVs in transmitted and non-transmitted chromosomes of the parents, it’s something like 31 gene-based CNVs in 154 parents (i.e., 20 percent of the parents have a gene-based deletion or duplication in the very illness-related pathways that are highlighted in the cases), which is at least as high a frequency as in the adult-onset schizophrenia sample in this study…and three times the frequency as found in the adult controls. This is not to say that such variants might not represent susceptibility genetic factors, or show variable penetrance between individuals, like other polymorphisms, and contribute to the complex genetic risk architecture, like other genetic variations that have been more consistently associated with schizophrenia. However, the CNV literature has tended to seek a more major effect connotation to the findings.

Comment by: William HonerSubmitted 28 March 2008
Posted 28 March 2008 I recommend the Primary Papers

As new technologies are applied to understanding the etiology and pathophysiology of schizophrenia, considering the clinical features of the cases studied and the implications of the findings is of value. The conclusion of the Walsh et al. paper, “these results suggest that schizophrenia can be caused by rare mutations….“ is worth considering carefully.

What evidence is needed to link an observation in the laboratory or clinic to cause? Recent recommendations for the content of papers in epidemiology (von Elm et al., 2008) remind us of the suggestions of A.V. Hill (Hill, 1965). To discern the implications of a finding, or association, for causality, Hill suggests assessment of the following:

1. Strength of the association: this is not the observed p-value, but a measure of the magnitude of the association. In the Walsh et al. study, the primary outcome measure, structural variants duplicating or deleting genes was observed in 15 percent of cases, and 5 percent of controls. But what is the association with? The diagnostic entity of schizophrenia, or some risk factor for the illness? Of interest, and noted in the Supporting Online Material, these variants were present in 7/15 (47 percent) of the cases with presumed IQ <80, but only 15/135 (11 percent) of the cases with IQ >80. Are the structural variants more strongly associated with mental retardation (within schizophrenia 47 percent vs. 11 percent) than with diagnosis (11 percent vs. 5 percent of controls, assuming normal IQ)? This is of particular interest in the context of the speculation in the paper concerning the importance of genes putatively involved with brain development in the etiology of schizophrenia.

2. Consistency of results in the literature across studies and research groups: there are now several papers examining copy number variation in schizophrenia, including a report from our group (Wilson et al., 2006). The authors of the present paper state that each variant observed was unique, and so consistency of the specific findings could be argued to be irrelevant, if the model is of novel mutations (more on models below). Undoubtedly, future meta-analyses and accumulating databases help determine if there is anything consistent in the findings, other than a higher frequency of any abnormalities in cases rather than controls.

3. Specificity of the findings to the illness in question: this was not addressed experimentally in the paper. However, the findings of more abnormalities in the putative low IQ cases, and the similarity of the findings to reports in autism and mental retardation, suggest that this criterion for supporting causality is unlikely to be met.

4. Temporality: the abnormalities should precede the illness. If DNA from terminally differentiated neurons harbors the same variants as DNA from constantly renewed populations of lymphocytes, then clearly this condition is met. While it seems highly likely that this is the case, it is worthwhile considering the possibility that DNA structure may vary between tissue types, or between cell populations. Even within human brain there is some evidence for chromosomal heterogeneity (Rehen et al., 2005).

5. Biological gradient: presence of a “dose-response” curve strengthens the likelihood of a causal relationship. This condition is not met within cases: only 1/115 appeared to have more than one variant. However, in the presumably more severe childhood onset form of schizophrenia, four individuals carried multiple variants, and the observation of a higher prevalence of variants overall. Still, the question of what the observations of CNV are associated with is relevant, since one of the inclusion/exclusion criteria for COS allowed IQ 65-80, and it is uncertain how many of these cases had some degree of intellectual deficit.

6. Plausibility: biological likelihood—quite difficult to satisfy as a criterion, in the context of the limits of knowledge concerning the mechanisms of illness of schizophrenia.

7. Coherence of the observation with known facts about the illness: the genetic basis of schizophrenia is quite well studied, and there is no dearth of theories concerning genetic architecture. However, a coherent model remains lacking. As examples, the suggestion is made that the observations concerning inherited CNVs in the COS cases are linked with a severe family history in this type of illness. This appears inconsistent with a high penetrance model for CNVs as suggested in the opening of the paper (presuming the parents in COS families are unaffected, as would seem likely). Elsewhere, CNVs are proposed by the authors to be related to de novo events, and an interaction with an environmental modifier, folate (and exposure to famine), is posited (McClellan et al., 2006). A model of the effects of CNVs, which generates falsifiable hypotheses is needed.

8. Experiment: the ability to intervene clinically to modify the effects of CNVs disrupting genes seems many years away.

9. Analogy: the novelty of the CNV findings is both intriguing, but limiting in understanding the likelihood of causal relationships.

The intersection of clinical realities and novel laboratory technologies will fuel the need for better translational research in schizophrenia for many, many more years.

The new study by Walsh et al. (2008), as well as recent data from other groups working in schizophrenia, autism, and mental retardation, make a strong case for including copy number variants as an important source of risk for neurodevelopmental phenotypes. These findings raise several intriguing new questions for future research, including: the degree of causality/penetrance that can be attributed to individual CNVs; diagnostic specificity; and recency of their origins. While these questions are difficult to address in the context of private mutations, one potential source of additional information is the examination of common, recurrent CNVs, which have not yet been systematically studied as potential risk factors for schizophrenia.

Still, the association of rare CNVs with schizophrenia provides additional evidence that genetic transmission patterns may be a complex hybrid of common, low-penetrant alleles and rare, highly penetrant variants. In diseases ranging from Parkinson's to colon cancer, the literature demonstrates that rare penetrant loci are frequently embedded within an otherwise complex disease. Perhaps the most well-known example involves mutations in amyloid precursor protein and the presenilins in Alzheimer’s disease (AD). Although extremely rare, accounting for <1 percent of all cases of AD, identification of these autosomal dominant subtypes greatly enhanced understanding of pathophysiology. Similarly, a study of consanguineous families in Iran has very recently identified a rare autosomal recessive form of mental retardation (MR) caused by glutamate receptor (GRIK2) mutations, thereby opening new avenues of research (Motazacker et al., 2007). In schizophrenia, we have recently employed a novel, case-control approach to homozygosity mapping (Lencz et al., 2007), resulting in several candidate loci that may harbor highly penetrant recessive variants. Taken together, these results suggest that a diversity of methodological approaches will be needed to parse genetic heterogeneity in schizophrenia.

In my mind, the study of CNVs in autism (and likely soon in schizophrenia/bipolar disorder, which are a little behind) is likely to put biological meat on the bones of illness etiology and finally lay to rest the annoyingly persistent taunts that genetics hasn’t delivered on its promises for psychiatric illness.

I don’t think it’s necessary at the moment to wring our hands at any inconsistencies between the Walsh et al. and previous studies of CNV in schizophrenia (e.g., Kirov et al., 2008). There are a number of factors which I think are going to influence the frequency, type, and identity of CNVs found in any given study.

1. CNVs are going to be found at the rare/penetrant/familial end of the disease allele spectrum—in direct contrast to the common risk variants which are the targets of recent GWAS studies. In the short term, we are likely to see a large number of different CNVs identified. The nature of this spectrum, however, is that there will be more common pathological CNVs which should be replicated sooner—NRXN1, APBA2 (Kirov et al., 2008), CNTNAP2 (Friedman et al., 2008)—and may be among some of these “low hanging fruit.” For the rarer CNVs, proving a pathological role is going to be a real headache. Large studies or meta-analyses are never going to yield significant p-values for rare CNVs which, nevertheless, may be the chief causes of illness for those few individuals who carry them. Showing clear segregation with illness in families is likely to be the only means to judge their role. However, we must not expect a pure cause-and-effect role for all CNVs: even in the Scottish t(1;11) family disrupting the DISC1 gene, there are several instances of healthy carriers.

2. Sample selection is also likely to be critical. In the Kirov paper, samples were chosen to represent sporadic and family history-positive cases equally. In the Walsh paper, samples were taken either from hospital patients (the majority) or a cohort of childhood onset schizophrenia. Detailed evidence for family history on a case-by-case basis was not given but appeared far stronger in the childhood onset cases. CNVs appeared to be more prevalent, and as expected, more familial, in the latter cohort. A greater frequency was also observed in the Kirov study familial subset.

3. Inclusion criteria are likely to be important—particularly in the more sporadic cases without family history. This is because CNVs found in this group may be commoner and less penetrant—they will be more frequent in cases than in controls but not exclusively found in cases. Any strategy, such as that used in the Kirov paper, which discounts a CNV based on its presence—even singly—in the control group is likely to bias against this class.

4. Technical issues. Certainly, the coverage/sensitivity of the method of choice for the “event discovery” stage is going to influence the minimum size of CNV detectable. However, a more detailed coverage often comes with a greater false-positive rate. Technique choice may also have more general issues. In both of the papers, the primary detection method is based on hybridization of case and pooled control genomes prior to detection on a chip. Thus, a more continuously distributed output may result—and the extra round of hybridization might bias against certain sequences. More direct primary approaches such as Illumina arrays or a second-hand analysis of SNP genotyping arrays may provide a more discrete copy number output, but these, too, can suffer from interpretational issues.

The other major implication of these and other CNV studies is the observation that certain genes “ignore” traditional disease boundaries. For example, NRXN1 CNVs have now been identified in autism and schizophrenia, and CNTNAP2 translocations/CNVs have been described in autism, Gilles de la Tourette syndrome, and schizophrenia/epilepsy. This mirrors the observation of common haplotypes altering risk across the schizophrenia-bipolar divide in numerous association studies. It might be the case that these more promiscuous genes are likely to be involved in more fundamental CNS processes or developmental stages—with the precise phenotypic outcome being defined by other variants or environment. The presence of mental retardation comorbid with psychiatric diagnoses in a number of CNV studies suggests that this might be the case. I look forward to the Venn diagrams of the future which show us the shared neuropsychiatric and disease-specific genes/gene alleles. It will also be interesting to see if the large deletions/duplications involving numerous genes give rise to more severe, familial, and diagnostically more defined syndromes or, alternatively, a more diffuse phenotype. Certainly, it has not been easy to dissect out individual gene contributions to phenotype in VCFS or the minimal region in Down syndrome.

We agree with the comments of Weinberger, Lencz and Malhotra, and Pickard, and the question raised by Honer about the extent to which the association may be more to mental retardation than schizophrenia. These new studies of copy number variation represent important advances, but need to be interpreted carefully.

We are now getting two different kinds of data on schizophrenia, which can be seen as two opposite poles. The first is from association studies with common variants, in which large numbers of people are required to see significance, and the strengths of the associations are quite modest. These kinds of vulnerability factors would presumably contribute a very modest increase in risk, and many taken together would cause the disease. By contrast, the “private” mutations, as identified by the Sebat study, could potentially be completely causative, but because they are present in only single individuals or very small numbers of individuals, it is difficult to be certain of causality. Furthermore, since some of them in the early-onset schizophrenia patients were present in unaffected parents, one might have to assume the contribution of a common variant vulnerability (from the other parent) as well.

If a substantial number of the private structural mutations are causal, then one might expect to have seen multiple small Mendelian families segregating a structural variant. The situation would then be reminiscent of the autosomal dominant spinocerebellar ataxis, in which mutations (currently about 30 identified loci) in multiple different genes result in similar clinical syndromes. The existence of many small Mendelian families would be less likely if either 1) structural variants that cause schizophrenia nearly always abolish fertility, or 2) some of the SVs detected by Walsh et al. are risk factors, but are usually not sufficient to cause disease. The latter seems more likely.

We think these two poles highlight the continued importance of segregation studies, as have been used for the DISC1 translocation. In order to validate these very rare “private” copy number variations, we believe that it would be important to look for sequence variations in the same genes in large numbers of schizophrenia and control subjects, and ideally to do so in family studies.

One very exciting result of the new copy number studies is the implication of whole pathways rather than just single genes. This highlights the importance of a better understanding of pathogenesis. The study of candidate pathways should help facilitate better pathogenic understanding, which should result in better biomarkers and potentially improve classification and treatment. In genetic studies, development of pathway analysis will be fruitful. Convergent evidence can come from studies of pathogenesis in cell and animal models, but this will need to be interpreted with caution, as it is possible to make a plausible story for so many different pathways (Ross et al., 2006). The genetic evidence will remain critical.

The idea that a proportion of schizophrenia is associated with rare chromosomal abnormalities has been around for some time, but it has been difficult to be sure whether such events are pathogenic given that most are rare. Two instances where a pathogenic role seems likely are first, the balanced ch1:11 translocation that breaks DISC1, where pathogenesis seems likely due to co-segregation with disease in a large family, and second, deletion of chromosome 22q11, which is sufficiently common for rates of psychosis to be compared with that in the general population. This association came to light because of the recognizable physical phenotype associated with deletion of 22q11, and the field has been waiting for the availability of genome-wide detection methods that would allow the identification of other sub-microscopic chromosomal abnormalities that might be involved, but whose presence is not predicted by non-psychiatric syndromal features. This technology is now upon us in the form of various microarray-based methods, and we can expect a slew of studies addressing this hypothesis in the coming months.

Structural chromosomal abnormalities can take a variety of forms, in particular, deletions, duplication, inversions, and translocations. Generally speaking, these can disrupt gene function by, in the case of deletions, insertions and unbalanced translocations, altering the copy number of individual genes. These are sometimes called copy number variations (CNVs). Structural chromosomal abnormalities can also disrupt a gene sequence, and such disruptions include premature truncation, internal deletion, gene fusion, or disruption of regulatory or promoter elements.

It is, however, worth pointing out that structural chromosomal variation in the genome is common—it has been estimated that any two individuals on average differ in copy number by a total of around 6 Mb, and that the frequency of individual duplications or deletions can range from common through rare to unique, much in the same way as other DNA variation. Also similar to other DNA variation, many structural variants, indeed almost certainly most, may have no phenotypic effects (and this includes those that span genes), while others may be disastrous for fetal viability. Walsh and colleagues have focused upon rare structural variants, and by rare they mean events that might be specific to single cases or families. For this reason, they specifically targeted CNVs that had not previously been described in the published literature or in the Database of Genomic Variants. The reasonable assumption was made that this would enrich for CNVs that are highly penetrant for the disorder. Indeed, Walsh et al. favor the hypothesis that genetic susceptibility to schizophrenia is conferred not by relatively common disease alleles but by a large number of individually rare alleles of high penetrance, including structural variants. As we have argued elsewhere (Craddock et al., 2007), it seems entirely plausible that schizophrenia reflects a spectrum of alleles of varying effect sizes including common alleles of small effect and rare alleles of larger effect, but data from genetic epidemiology do not support the hypothesis that the majority of the disorder reflects rare alleles of large effect.

Walsh et al. found that individuals with schizophrenia were >threefold more likely than controls to harbor rare CNVs that impacted on genes, but in contrast, found no significant difference in the proportions of cases and controls carrying rare mutations that did not impact upon genes. They also found a similar excess of rare structural variants that deleted or duplicated one or more genes in an independent series of cases and controls, using a cohort with childhood onset schizophrenia (COS).

The results of the Walsh study are important, and clearly suggest a role for structural variation in the etiology of schizophrenia. There are, however, a number of caveats and issues to consider. First, it would be unwise on the basis of that study to speculate on the likely contribution of rare variants to schizophrenia as a whole. It is likely correct that, due to selection pressures, highly penetrant alleles for disorders (like schizophrenia) that impair reproductive fitness are more likely to be of low frequency than they are to be common, but this does not imply that the converse is true. That is, one cannot assume that the penetrance of low frequency alleles is more likely to be high than low. Thus, and as pointed out by Walsh et al., it is not possible to know which or how many of the unique events observed in their study are individually pathogenic. Whether individual loci contribute to pathogenesis (and their penetrances) is, as we have seen, hard to establish. Estimating penetrance by association will require accurate measurement of frequencies in case and control populations, which for rare alleles, will have to be very large. Alternatively, more biased estimates of penetrance can be estimated from the degree of co-segregation with disease in highly multiplex pedigrees, but these are themselves fairly rare in schizophrenia, and pedigrees segregating any given rare CNV obviously even more so.

As Weinberger notes, the case for high penetrance (at the level of being sufficient to cause the disorder) is also undermined by their data from COS, where the majority of variants were inherited from unaffected parents. This accords well with the observation that 22q11DS, whilst conferring a high risk of schizophrenia, is still only associated with psychosis in ~30 percent of cases. It also accords well with the relative rarity of pedigrees segregating schizophrenia in a clearly Mendelian fashion, though the association of CNVs with severe illness of early onset might be expected to reduce the probability of transmission.

Third, there are questions about the generality of the findings. Cases in the case control series were ascertained in a way that enriched for severity and chronicity. Perhaps more importantly, the CNVs were greatly overrepresented in people with low IQ. Thus, one-third of all the potentially pathogenic CNVs in the case control series were seen in the tenth of the sample with IQ less than 80. The association between structural variants and low IQ is well known, as is the association between low IQ and psychotic symptoms, and it seems plausible to assume that forms of schizophrenia accompanied by mental retardation (MR) are likely to be enriched for this type of pathogenesis. The question that arises is whether the CNVs in such cases act simply by influencing IQ, which in turn has a non-specific effect on increasing risk of schizophrenia, or whether there are specific CNVs for MR plus schizophrenia, and some which may indeed increase risk of schizophrenia independent of IQ. In the case of 22q11 deletion, risk of schizophrenia does not seem to be dependent on risk of MR, but more work is needed to establish that this applies more generally.

Another reason to caution about the generality of the effect is that Walsh et al. found that cases with onset of psychotic symptoms at age 18 or younger were particularly enriched for CNVs, being greater than fourfold more likely than controls to harbor such variants. There did remain an excess of CNVs in cases with adult onset, supporting a more general contribution, although it should be noted that even in this group with severe disorder, this excess was not statistically significant (Fisher’s exact test, p = 0.17, 2-tailed, our calculation). The issue of age of onset clearly impacts upon assessing the overall contribution CNVs may make upon psychosis, since onset before 18, while not rare, is also not typical. A particular contribution of CNVs to early onset also appears supported by the second series studied, which had COS. However, this is a particularly unusual form of schizophrenia which is already known to have high rates of chromosomal abnormalities. Future studies of more typical samples will doubtless bear upon these issues.

Even allowing for the fact that many more CNVs may be detected as resolution of the methodology improves, the above considerations suggest it is premature to conclude a substantial proportion of cases of schizophrenia can be attributed to rare, highly penetrant CNVs. Nevertheless, even if it turns out that only a small fraction of the disorder is attributable to CNVs, as seen for other rare contributors to the disorder (e.g., DISC1 translocation), such uncommon events offer enormous opportunities for advancing our knowledge of schizophrenia pathogenesis.

Walsh et al. claim that rare and severe chromosomal structural variants (SVs) (i.e., not described in the literature or in the specialized databases as of November 2007) are highly penetrant events each explaining a few, if not singular, cases of schizophrenia.

However, their definition of rareness is questionable. Indeed, it is unclear why SVs that are rare (<1 percent) but previously described should be omitted from their analysis. In addition, contrary to their own definition of rareness, the authors included in the COS sample several SVs that have been previously mentioned in the literature (e.g. “115 kb deletion on chromosome 2p16.3 disrupting NRXN1”). Furthermore, some of these SVs (entire Y chromosome duplication) are certainly not rare (by the authors’ definition), nor highly penetrant with regard to psychosis (Price et al., 1967). Finally, as their definition of rareness depends on a specific date, the results of this study will change over time.

As to the assessment of severity, it can equally be concluded from table 2 and using their statistical approach that "patients with schizophrenia are significantly more likely to harbor rare structural variants (6/150) that do not disrupt any gene compared to controls(2/268) (p = 0.03)", thus contradicting their claim. In fact, had they used criteria in the literature (Lee et al., 2007; (Brewer et al., 1999) (i.e., deletion SVs are more likely than duplications to be pathogenic) and appropriate statistical contrasts, deletions are significantly (p = 0.02) less frequent in patients (5/23) than in controls (9/13) who have SVs. In addition, the assumption of high penetrance is questionable given the high level (13 percent) of non-transmitted SVs in parents of COS patients. Is the rate of psychosis proportionately high in the parents? From the data presented, we know that only 2/27 SVs in COS patients are de novo and that “some” SVs are transmitted. Adding this undetermined number of transmitted SVs to the reported non-transmitted SVs will lead to an even larger proportion of parents carrying SVs. Disclosing the inheritance status of SVs in COS patients along with information on diagnoses in parents from this “rigorously characterised cohort,” represents a major criterion for assessing the risk associated with these SVs.

Consequently, it appears that the argument of rareness is rather idiosyncratic and contains inconsistencies, and the one of severity is very open to interpretation. Most importantly, it should be emphasized that amalgamated gene effects at the population level do not allow one to conclude that any single SV actually contributes to schizophrenia in an individual. Thus it is unclear how this study of grouped events differs from the thousands of controversial and underpowered association studies of single genes.

The paper by Talkowski and colleagues describes the application of cutting edge genomics techniques to the molecular characterisation of multiple balanced chromosomal abnormalities (BCAs) linked to autism, autism spectrum disorders, and general neurodevelopmental disorders. In a single publication it has probably assigned more candidate genes than the entire conventional cytogenetic output from schizophrenia and autism in the preceding 15 years.

The authors carry out a great deal of complementary genomic analyses which add to the strength of their argument that these genes are indeed causally involved in illness. Without these additional data there would be one potential criticism of the paper in that the same power of analysis was not applied to BCAs in healthy controls. This is an important ascertainment issue because previous studies have not only identified disrupted genes in the healthy population (Baptista et al., 2005) but also shown that CNVs deregulating specific genes may only show an increased—as opposed to exclusive—representation in the ill population.

The observed overlaps between some of the identified BCA genes in ASD/neurodevelopmental disorders and those identified in GWAS studies of schizophrenia and bipolar disorder is fascinating but may be a double-edged sword. On the one hand, support for rare genetic contributors (CNVs/sequence variants/BCAs) to complex genetic disorders has often been drawn from those that are co-incident between studies. In that respect, this study is remarkable for highlighting the same genes from methods that detect very different mutation types. I’m genuinely surprised that there appears to be a convergence of ancient (read subject to evolutionary selection/population effects) and recent (meaning random) mutations. On the other hand, there is the disconcerting possibility that schizophrenia GWAS are only powered to detect the causes of blunt neurodevelopmental disturbances (which are perhaps less sensitive to issues of diagnostic categorisation) and not the fine-grained genetic hits that determine a precise clinical endpoint. If this is the case then we could end up with a situation where the genotypic distance between disorders is apparently much less than the phenotypic distance. This is most likely an extreme outcome that will be remedied once the genomic analysis of complex genetic disorders is able to factor in the composite effects of BCAs, CNVs, rare SNPs, and common SNPs—at the level of the single individual, and perhaps conditioned on the presence of big neurodevelopmental hits.

Quite logically, the presence of genes spanning diagnoses has been explained in terms of shared predisposition derived from early neurodevelopmental insults that are subsequently pushed down diagnostic pathways by other genetic or environmental factors. However, this assumption needs formal testing. The problem is reminiscent of the debate that circled the early use of constitutive mouse knockouts: how is it possible to disentangle developmental from adult functional phenotypes in a null? The advent of inducible Cre-LoxP technologies allowed that question to be directly addressed and may be the means to test the neurodevelopmental contribution of diagnosis-spanning candidate genes such as TCF4.

Could the approach detailed in this paper be applied directly to schizophrenia? It would certainly add substantially to the ‘confirmed’ gene list and would detect any reciprocal relationships with ASD/neurodevelopmental disorders. One issue is that ASD appears to have a higher overall incidence of chromosomal and genomic structural rearrangements than schizophrenia, but perhaps the greater question is availability of an appropriate sample set. The concerted cytogenetic screening that took place in Scotland coupled with an ability to cross-reference these findings with incidence of psychiatric disorder was instrumental in the discovery of DISC1 and other genes in Scotland (Muir et al., 2008)
but this resource is now largely exhausted of relevant BCAs. To my knowledge, the Danish registry represents the best bet for such an approach to succeed for schizophrenia (Bache et al., 2006).

In this exceptional paper, the authors combined new technology with old-school genomics to deliver convergent data about the genomic regions that predispose to neuropsychiatric disorders. The first goal of psychiatric genetics is to identify the “parts list,” an enumeration of the genes and genetic loci whose alteration clearly and unequivocally alters risk. The results of this intriguing paper connect rare and powerful genomic disruptions with loci identified via common variant genomewide association screens.

A classical approach in human genetics is to study affected individuals with balanced translocations. Using next-generation sequencing, these authors identified the precise locations of 38 rare balanced chromosomal abnormalities in subjects with neurodevelopmental disorders. They identified 33 disrupted genes, of which 22 were novel risk loci for autism and neurodevelopmental disorders. The other disrupted genes included many that had previously been identified by genomic searches for rare variation and common variation (e.g., AUTS2, CHD8, TCF4, and ZNF804A).

The authors then sought secondary genomic support for disease association with these 33 risk loci by analyzing a large collection of psychiatric GWAS data. They found an increased burden of copy number variants (CNVs) among cases as well as a significant enrichment of common risk alleles among both autism and schizophrenia cases. This research suggests that autism and neurodevelopmental disorders may have commonalities with psychiatric disorders such as schizophrenia at the molecular level, underscoring the complexity of genetic contribution to these conditions.

CNVs discovered from microarrays are mainly large, rare CNVs spanning multiple genes. Exome sequencing is limited to coding regions of the genome. In contrast, as illustrated in Talkowski et al. (2012), it is possible to identify individual lesions with nucleotide resolution in both coding and non-coding regions. Thus, this research suggests that sequencing individuals with pathogenic balanced translocations could provide a complementary strategy for mutation identification and gene discovery.

The experimental procedures were technically well done; all BCA breakpoints were confirmed by PCR and capillary sequencing. In seeking the secondary genomic support, the authors were keen on evaluating and eliminating the possibility for any confounding factors that may cause spurious association. For example, CNV burden analysis was conducted with respect to differential sensitivities from microarrays, and all results remained robust to various subset analyses and to one million simulations designed to establish empirical significance. To examine the potential for spurious enrichment of common risk alleles, the authors additionally conducted identical analysis in phenotype-permuted datasets from well-powered GWAS data for schizophrenia and autism as well as in well-powered GWAS data for eight unrelated traits, and therefore eliminated unforeseen confounders.

Impressively, many of the loci identified here now have convergent genomic results with support across multiple different samples and technical approaches. For example, TCF4 harbors common variation identified via GWAS, a Mendelian disorder, and now a gene disruption. These convergent genomic results markedly increase confidence that TCF4 is truly in the “parts list” for neurodevelopmental disorders. In contrast, there remain multiple questions about the genomic evidence for DISC1, where such convergence has not been achieved.

This paper also provides important results relevant to resolving the rare “versus” common variation debate. This appears to be a false dichotomy where, often, both rare and common variations contribute to the parts lists for these disorders.

Balanced chromosomal abnormalities (BCAs) provide extremely useful alterations for linking of specific loci with psychiatric conditions, because they exert penetrant effects and localize to specific genes. The recent study by Talkowski et al. (2012) used direct sequencing of breakpoints, based on 38 subjects, to generate a set of genes with putative links to different neurodevelopmental disorders, broadly construed as including autism spectrum disorders, intellectual disability, and/or developmental and other delays.

One of the most striking results from their study was the presence, in their set of breakpoint-altered genes, of five genes that have been associated from other work with schizophrenia and related psychotic-affective spectrum disorders (such as bipolar disorder and major depression), including TCF4, ZNF804A, PDE10A, GRIN2B, and ANK3. These results suggest, according to the authors, the presence of shared genetic etiology for ASD, schizophrenia, and other neurodevelopmental disorders (mainly developmental delays). The authors also show overlap of their gene list with results from CNV and GWAS of autism and schizophrenia, further suggesting genetic links between these two conditions.

Do these results mean that autism and schizophrenia share genetic risk factors? Perhaps, but also perhaps not. Two important caveats apply.

These findings suggest that the subjects in Talkowski et al. (Talkowski et al., 2012), (most of them children, for individuals with age data given) who harbor alterations to schizophrenia-associated genes may, in fact, be severely premorbid for schizophrenia. Diagnoses of ASD (commonly PDD-NOS) in such individuals may represent either false positives (Eliez, 2007
; Feinstein and Singh, 2007), or true positives, with ASD as a developmental stage followed, in some individuals, by schizophrenia. This latter conceptualization considers autism as akin to childhood schizophrenia, a view which contrasts sharply with the classic criteria derived from Kanner (Kanner, 1943), Asperger (1991) and Rutter (Rutter, 2000, Rutter, 1972, Rutter, 1978), who consider autism as a lifelong condition present from early childhood. Of course, diagnosing premorbidity to schizophrenia as such is challenging, but if any data can help, it is data from highly penetrant alterations such as CNVs and BCAs, as well as from biological and neurological (rather than just behavioral) phenotypes.

Second, association to an overlapping set of genes need not make two disorders similar, or similar in their genetic etiology. For example, as noted by Talkowski et al. (Talkowski et al., 2012), variation in TCF4 has been associated with both Pitt-Hopkins syndrome and schizophrenia, but these conditions show essentially no overlap in phenotypes. Similar considerations apply to CACNA1C, linked to the autism-associated Timothy syndrome (via an apparent gain of function) as well as to schizophrenia and bipolar disorder. A key to sorting out the huge clinical and genetic heterogeneity in autism, and in schizophrenia, is subsetting of cases by similarity in alterations to pathways and phenotypes. Lumping of autism with schizophrenia, based on overlap in risk loci without consideration of the nature of the overlap, will make such subsetting all the more difficult.

Data on genes disrupted by balanced translocations are tremendously useful, but their usefulness will, as for other data such as CNVs, be circumscribed by diagnostic considerations, especially when the subjects are children. Bearing in mind the possibility that some childhood diagnoses may represent false positives, and that overlap in genes need not mean overlap in causation, should help in moving the study of both autism and schizophrenia forward.